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These deep learning algorithms outperformed a panel of 11 pathologists

During a 2016 simulation exercise, researchers evaluated the ability of 32 different deep learning algorithms to detect lymph node metastases in patients with breast cancer. Each algorithm’s performance was then compared to that of a panel of 11 pathologists with time constraint (WTC). Overall, the team found that seven of the algorithms outperformed the panel of pathologists, publishing an in-depth analysis in JAMA.

Radiology professor uses nanotechnology to develop potential cancer treatment

Kattesh V. Katti, MScEd, PhD, a professor of radiology and physics at the University of Missouri in Columbia, specializes in planet-friendly nanotechnology and has developed a potential cancer treatment that “uses no toxic chemicals and leaves zero toxic waste.”

Radiologists scan mummy to better understand the past

A 2,000-year-old Egyptian mummy named Hen, from the Cazenovia Public Library in New York, was transferred for five hours to the Crouse Hinds Hospital in Syracuse for medical tests and technological exams that could potentially offer new information about the mummy and humanity in that time.

RSNA in review: Radiologists ready to make the most of AI

At RSNA 2017 in Chicago, artificial intelligence (AI) and deep learning technologies were everywhere. Attendees rushed to learn as much as possible about AI, countless educational sessions touched on the topic and exhibitors made sure to mention it in their booths as much as possible. I wouldn’t quite say AI took over the show like some have suggested, but it did make quite an impression on everyone walking through the doors of McCormick Place.

Novel needle technique takes optical-ultrasound guidance inside the heart

British researchers have successfully outfitted a needle with optical fibers capable of transmitting and receiving ultrasonic pulses within the heart during minimally invasive cardiac surgeries. The achievement may represent a new way to optically image tiny tissue targets in real time and at high resolution throughout the body.

RSNA 2017: A radiologist’s guide to the differences between Facebook, Twitter and other social media platforms

The buzz around social media in radiology has skyrocketed in recent years, with more and more departments, private practices and specialists starting to use using the various platforms to their advantage. Of course, it’s about more than just using sites such as Facebook, Twitter and Instagram; to get the most out of these resources, one must also learn the differences between them.

RSNA 2017: AI has potential to match the hype

Interest in artificial intelligence (AI) and machine learning at RSNA 2017 seems like it’s unprecedented—but the increased attention is quantifiable. More than 100 sessions delve into the topic at this year’s show in Chicago. Two years ago, less than 10 touched on such concepts.

What do Twitter users have to say about lung cancer?

Social media platforms have quickly become dominant outlets to discuss healthcare, including lung cancer-specific topics across the cancer prevention and control continuum.

Interoperability in Radiology: A Game of Inches

The health IT holy grail of nationwide interoperability remains top of mind in theory yet miles away in practice. The daunting distance of the road ahead was thrown into sharp relief in early October, when Health Affairs published American Hospital Association (AHA) survey data from 2015 showing that two of three U.S. hospitals can’t locate, retrieve, send and/or meaningfully integrate the electronic medical records (EMRs) of patients who received care at other provider sites (Health Aff (Millwood). 2017 Oct 1;36(10):1820-1827). 

Examining AI’s Impact on Breast Imaging

By Working Closely with AI Technologies, Radiologists Are Making Considerable Strides in Breast Cancer Treatment